Edited by Stephane Hess and Andrew Daly
Chapter 20: Bayesian estimation of random utility models
Conjoint studies and their Bayesian estimation are remarkably intertwined. Luce and Tukey (1964) originated conjoint analysis for measuring judgment and perception in mathematical psychology. They proposed a system to measure constituent components of multi-attribute stimuli from subjects’ ordering of the stimuli. Meanwhile in economics, Lancaster (1966) proposed a theory of consumer choice that decomposed the utility of goods into the utility for their attributes. Green and Rao (1971) synthesized these two ideas to decompose the desirability of product1 attributes from subjects’ rankings of the products. For example, three attributes for hotels are room comfort, business centres and swimming pools. Based on a subject’s ranking of hotels, the researcher can measure the preferences for each attribute. Then a hotel chain can use this information to design hotels for different segments of customers. For instance, business travellers may appreciate business centers but not swimming pools, while families travelling with children prefer swimming pools to business centres. Wind et al. (1989) conducted such a study to design Courtyard by Marriott. The connection between Bayesian inference and conjoint analysis runs deeper than merely providing practical, effective estimation and measurement methods. They both have foundations in utility theory. Random utility theory (RUT), introduced by McFadden (1974) and foreshadowed by Aitchison and Bennet (1970), provides the economic foundation for conjoint analysis.
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